Different Alexa services use different names for the same types of data, which makes it hard to track references across dialogues. By learning correlations between data types, a machine learning model can make better decisions about which references to track from one round of dialogue to the next.

Treating news headlines and Wikipedia section headers as "search terms" and the associated texts as search results enables the training of neural search engines with less need for manually annotated data.

Alexa scientists use machine learning to improve the performance of "multilabel classifiers", which classify data according to several categories at once — identifying multiple objects in an image, for instance, or multiple topics touched on by a single article.

Natural-language-understanding system that includes both a generic model for a language and several locale-specific models improves accuracy by an average of 59% over individual locale-specific models.